Evolutionary behaviour in ``animat'' or physical-agent models has been explored by several researchers, using a number of variations of the genetic algorithmic approach. Most have used a bio-inspired mutation/evolution of low-level behaviours or model properties and this leads to large and mostly ``uninteresting'' model phase-spaces or fitness landscapes. Instead we consider individual animats that evolve their priorities amongst short-lists of high-level behavioural rules rather than of lower-level individual instructions. This dramatically shrinks the combinatorial size of the fitness landscape and focuses on variations within the ``interesting'' regime. We describe a simple evolutionary survival experiment, which showed that some rule-priorities are drastically more successful than others. We report on the success of the rule-priority evolutionary approach for our predator-prey animat model and consider how it would apply to more general agent-based models.
Keywords: behaviour rules; priorities; evolution; animat models.
Full Document Text: PDF version.
Citation Information: in Proc. Second Australian Conference on Artificial Life (ACAL 2005), Sydney, Australia, 2005.
BiBTeX reference:
@inproceedings{CSTN-020,
Address = {Sydney, Asutralia},
Author = {K. A. Hawick, H. A. James and C.J.Scogings},
Booktitle = {Proc. Second Australian Conference on Artificial Life (ACAL 2005)},
Month = {December},
Title = {Roles of Rule-Priority Evolution in Animat Models},
Year = {2005}
}
\bibitem{CSTN-020}
K.A.Hawick, H.A.James and C.J.Scogings,
Roles of Rule-Priority Evolution in Animat Models,
Technical note CSTN-020 and in
Proc. Second Australian Conference on Artificial Life (ACAL 2005), Sydney, Australia, 2005.